What Building PPI.Studio Taught Me About Governing AI Coding Agents

Описание и применение

Построение PPI.Studio показало, что AI‑кодеры быстро генерируют код, но часто теряют смысловые детали. Внедрение строгой системы контроля, где человек владеет производственным кодом, а AI лишь предлагает изменения с полным описанием замены, обеспечивает надёжность и прозрачность.

Задача

AI coding agents can produce code quickly but often compress or misrepresent technical details, leading to incorrect architectural decisions.

Результат

The governance framework prevented semantic compression errors, ensured complete replacement paths, and maintained human accountability, turning AI agents into reliable engineering accelerators.

Рабочий процесс

• 1. Define a clear human‑owned production code base under src/main/.
• 2. Use src/test/ as the agent‑maintained proving ground.
• 3. Treat interfaces and contracts as change‑control boundaries with API impact analysis.
• 4. Enforce constructors to perform dependency assignment only.
• 5. Require every important claim to have an evidence class (Implemented, Tested, etc.).
• 6. When an agent suggests a change, provide the full replacement path and affected classes.
• 7. Conduct regular audits to verify build success and identify incomplete modules.
• 8. Maintain a closed‑loop workflow: evidence normalization → conflict detection → scoped analysis → recommendation → scientist review → assay feedback → updated evidence.

Доказательства

The failure was not a fabricated SDK boundary. It was semantic compression.

Источник

The responsible answer must identify the exact class, method, registration mechanism, and execution path that makes it happen.

Источник